RF -金融市場(chǎng)中的信息及其實(shí)際影響-Information in Financial Markets and Its Real Effects

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ReviewofFinance,2023,1–32

/10.1093/rof/rfac052

AdvanceAccessPublicationDate:10August2022

InformationinFinancialMarketsandItsRealEffects*

ItayGoldstein

WhartonSchool,UniversityofPennsylvaniaandNBER,USA

Abstract

Financialmarketshaveacentralroleinallocatingresourcesinmoderneconomies.Oneofthemainfunctionsof?nancialmarketsisthediscoveryofinformation.Thisinformationinturnhelpsguidedecisionsintherealsideoftheeconomy.Thelitera-tureonthe“feedbackeffect”of?nancialmarketsexploresthischannel.Empiricalworktriestoidentifytheinformationalfeedbackfrommarketstocorporatedeci-sions.Theoreticalworkexploresimplicationsthatthisfeedbackeffecthasfortheequilibriumin?nancialmarketsandforeconomicef?ciency.Currenttrendsininfor-mationtechnologyundertheFinTechrevolutionchangethenatureofinformationprocessingin?nancialmarketsandsomaychangethenatureofthefeedbackeffect.Inthisarticle,Ireviewthemainthemesofthisdevelopingliteratureandconnectthemtothecurrentinformationrevolution.Ialsodiscussdirectionsforfutureresearch.

*ThisarticleisbasedonmykeynotespeechattheEuropeanFinanceAssociationAnnualMeetinghostedbyBocconiUniversityin2021.Overthelastfewyears,Ihavegivenkeynotespeechesandplenarysessionsrelatedtothesamethemein:FinanceTheoryGroupSummerSchool(2017),HongKongUniversityofScienceandTechnologyFinanceSymposium(2017),TsinghuaUniversityInternationalCorporateGovernanceConference(2018),FinanceForuminMadrid(2019),SwedishHouseofFinanceConferenceonFinancialMarketsandCorporateDecisions(2019),CambridgeCorporateFinanceTheorySymposium(2019),UniversityofTexasatAustin(2019),FinancialManagementAssociationResearchIdeasSession(2019),INSEAD(2019),SantiagoFinanceWorkshop(2019),GlobalVirtualSeminarSeriesonFinTechatGeorgetownUniversity(2020),GreaterChinaAreaFinanceConference(2020),Mid-AtlanticResearchConferenceinFinanceatVillanovaUniversity(2021),IndianSchoolofBusinessSummerResearchConference(2021),ConferenceonFinancialEconomicsandAccountingatIndianaUniversity(2021),DukeAccountingTheorySummerSchool(2022),andEIASMWorkshoponAccountingandEconomicsinErasmusUniversity(2022).Ithankthemanyparticipantsintheseeventsfornumerousexcellentcommentsandhelpfuldiscussions.Iamalsoparticularlygratefultothefollowingpeopleforreadingadraftofthisarticleandcommentingonit:SimonaAbis,RalphBoleslavsky,QiChen,OlivierDessaint,EspenEckbo,AlexEdmans,ThierryFoucault,LaurentFresard,RayGao,AlexanderGuembel,SudarshanJayaraman,WeiJiang,XuewenLiu,StevenChongXiao,XixiXiao,LiyanYang,ChristinaZhu,LuoZuo,andananonymousreferee.Allremainingerrorsaremine.

VCTheAuthor(s)2022.PublishedbyOxfordUniversityPressonbehalfoftheEuropeanFinanceAssociation.Allrightsreserved.Forpermissions,pleaseemail:journals.permissions@

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Keywords:Financialmarkets,Corporate?nance,Information,Feedbackeffect,Financialtechnology

JELclassi?cation:G10,G30,D80

ReceivedJuly5,2022;acceptedJuly11,2022byEditorAlexEdmans.

1.Introduction:TheFeedbackEffectfromFinancialMarketstotheRealEconomy

1.1InformationinFinancialMarkets

Oneofthebasicpremisesinfinancialeconomicsisthatpricesinfinancialmarketsareveryinformativeaboutthefundamentalsoftheunderlyingassets.Pricesaggregateinformationfrommanydifferentindividualsandinstitutions,whotradeforprofitmotives,andsohaveanaturalincentivetotradeoninformativesignals.Throughthetradingprocess,marketpri-cesthenaggregateandreflectthedifferentsignals,creatingapowerfulsourceofinforma-tion,whichisdifficulttogenerateinotherways.

Theideathatpricesareausefulsourceofinformationgoesfarbackineconomicsandisoftenassociatedwith

Hayek(1945)

.Hereferredtopricesmoregenerally,forexample,thoseofgoodsandservices.Thepowerfulinformationalrolepricesplayaccordingtohimissimilartothatinfinancialmarkets,comingfromthefactthattheyaggregatepiecesofin-formationfromdifferentmarketparticipants.Thisviewofthepowerfulroleofmarketsasinformationprovidershasledmanyeconomistssincethentoadvocateforusingpricesasaprimarysourceofinformationforimportantdecisions.Suchisthepushforestablishingpredictionmarketsforimportantevents(see

WolfersandZitzewitz,2004

)andusingthein-formationtheyproduceinadvance.

Whilemarketsingeneralcanbepowerfulininformationprovision,itisdifficulttocom-petewithfinancialmarketsinthisrespect.Withthelevelofliquidity,thesophisticationofparticipants,andthehugeattentiontheyaregetting,financialmarketsareprimecandidatestoprovideinformativesignals.Moreover,overtheyears,financialmarketshavebecomemoreliquid,marketparticipantsmoresophisticated,andinformationaroundfinancialmarketsmorewidelyavailable.Anaturalconjecturethenisthatmarketpricesshouldhavebecomeevenmoreinformative.Theanalysisin

Bai,Philippon,andSavov(2016)

largelysupportsthisconjecture,while

Farboodietal.(2022)

concludethatthisisthecaseonlyforasubsetofthefirms,namelythelargegrowthfirms.Thinkingforwardabouttheriseoffi-nancialtechnologiesthatmakemarketsevenmoresophisticatedandthesourcesofinfor-mationavailableevenricherthanbefore,manywonderwhethermarketsignalswillbecomeevenmorepowerful.Inanycase,animportantquestioniswhattheimplicationsofmarketinformationfortherealeconomyare.Thisiswherethefeedbackeffectcomesin.

1.2LearningfromMarketPricesintheRealEconomy

Ifpricesareindeedsuchapowerfulsourceofinformation,asimpleextensionoftheargu-mentwouldsuggestthattheyshouldbeanimportantguideforproductionandinvestmentdecisions,orresourceallocationmorebroadly.Theliteratureonthe“feedbackeffect”offi-nancialmarketsbuildsonthispremise.Theideaisthataggregatinginformationfromdif-ferentpartsofthemarket,financialassetpricescanprovideusefulsignalstodecisionmakersintherealsideoftheeconomy.Thesedecisionmakerswillthenusethepricesignal

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whenmakingdecisionsthataffectthecashflowsofthefirmswhosesecuritiesaretradedinthemarket.

Whoarethedecisionmakersintherealsideoftheeconomywholearnfrommarketpri-ces?Theliteraturehasfocusedmostlyonmanagers.Mostoftheempiricalevidence,someofwhichwillbereviewedinthenextsection,isaboutthem.Thisisnaturalgiventhatman-agersarethemaindecisionmakersshapingthefutureofthecorporationandtheyareclose-lytunedtothemarketpricesoftheirfirms’stocks.Whenmakingbiginvestmentdecisions,theyarethusexpectedtotakealookatwhatmarketpricessayandincorporatethisfeed-backintotheirdecisions.However,thescopeofthefeedbackeffectisnotlimitedtomanagers.

Oneprimeexampleisregulators,whotakeactionsthataffectfirms’valuesandareknowntopayattentiontomarketprices.

Faure-Grimaud(2002)

providesniceexamplesofthewayUKregulatorsusemarketpricesofregulatedfirmstochangethelevelofregula-tion.Inthecontextofbanksupervision,thisisexplicitlyacknowledgedandencouragedbyregulators.Forexample,GaryStern,theformerPresidentofMinneapolisFedprovidedthelogicclearlyinthefollowingquote:

Marketdataaregeneratedbyaverylargenumberofparticipants.Marketparticipantshavetheirfundsatriskofloss.Amonetaryincentiveprovidesaperspectiveonrisktakingthatisdif-ficulttoreplicateinasupervisorycontext.Unlikeaccounting-basedmeasures,marketdataaregeneratedonanearlycontinuousbasisandtoaconsiderableextentanticipatesfutureperform-anceandconditions.Rawmarketpricesarenearlyfreetosupervisors.Thischaracteristicseemsparticularlyimportantgiventhatsupervisoryresourcesarelimitedandarediminishingincom-parisontothecomplexityoflargebankingorganizations.1

BenBernanke,theformerChairmanoftheFederalReserveSystem,alsoacknowledgedclearlythatmarketpricesplayanimportantinformationalroleinpolicymaking:

...policymakerswatchfinancialmarketscarefullyforanotherreason,whichisthatassetpricesandyieldsarepotentiallyvaluablesourcesoftimelyinformationabouteconomicandfinancialconditions.Becausethefuturereturnsonmostfinancialassetsdependsensitivelyoneconomicconditions,assetprices—ifdeterminedinsufficientlyliquidmarkets—shouldembodyagreatdealofinvestors’collectiveinformationandbeliefsaboutthefuturecourseoftheeconomy.2

Anotherexampleiscreditors.Whengivingcredittofirms,theyuseeverypieceofinforma-tionavailable,and,forpublicfirms,thisincludesthestockprice.Tobeginwith,creditrat-ingagencies,whoseratingshavealargeeffectoncreditors’decisions,areknowntopayattentiontomarketpricesofthefirms’securities,eventhoughtheysupplementthemwiththeirowninformation(

Gredil,Kapadia,andLee,2022

).Theconcernaboutcreditors’reac-tiontostockpriceshasbeenkeyinimposingrestrictionsorbansonshortsellingacrosscountriesandepisodesofmarketstress.Forexample,whenimposingbansonshortsalesofstocksoffinancialinstitutionsinthecrisisof2008,theU.S.SecuritiesandExchangeCommission(SEC)explained

Undernormalmarketconditions,shortsellingcontributestopriceefficiencyandaddsliquiditytothemarkets.Atpresent,itappearsthatunbridledshortsellingiscontributingtotherecent,suddenpricedeclinesinthesecuritiesoffinancialinstitutionsunrelatedtotruepricevaluation.

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Financialinstitutionsareparticularlyvulnerabletothiscrisisofconfidenceandpanicsellingbe-causetheydependontheconfidenceoftheirtradingcounterpartiesintheconductoftheircorebusiness.3

Acommoncounterargumenttothechanneloflearningfrompricesisthatmanydecisionmakerswhohavesignificantimpactonthevalueofthefirmshouldhavemoreinformationabouttheunderlyingfundamentalsthantheparticipantsinthefinancialmarket.Thisises-peciallythecasewhenthinkingaboutmanagers,whohaveaccesstofirst-handinformationthatisdifficultforotherstoobtain.However,whileitiscertainlytruethatthemanagermaybemoreinformedthananyotherindividualinthefinancialmarket,thepowerofthemarketisinaggregatingpiecesofinformationfrommanydifferenttraders.Inaddition,themarketispowerfulbecausemarketparticipantshaveinformationondifferentdimensionsthatcanberelevanttofirms’decisions.Aslongasthereissomeinformationthatmanagersdonothave—whichsurelymustbethecase—thentheyshouldrationallyupdatebasedonmarketprices.

Thisalsohasimplicationsforwhatkindofinformationmanagerswillattempttolearnandwhenthefeedbackeffectwillbemostrelevant.Biginvestmentdecisions,suchasanac-quisitionorenteringanewgeographicalregion,arebasedonspeculationsaboutfuturesyn-ergies,competition,anddemandforthefirms’products.Whilemanagersarewellinformedaboutassetsinplace,theyneedtobasesuchdecisionsonspeculativeassumptionsaboutthesefuturedevelopments.Thesameistruewhenthefirmiscontemplatingenteringnewactivitiesthataredevelopedintheeconomy.OnecanthinkofthedevelopmentoftheInternetinthe1990soractivitiesrelatedtofinancialtechnologiesorsustainabilitytoday.Similarly,managersmaybelimitedrelativetooutsidersinevaluatingtheimplicationsofmacroeconomicdevelopmentsontheirfirms.

Hutton,Lee,andShu(2012)

,forexample,findthatanalystshaveaninformationaladvantageovermanagerswhenitcomestotheef-fectofthemacroconditionsonthefirm.Overall,inallthesecases,internalinformationislimited,andthefirmcanbenefitfromsomeoutsideperspective.Thisoutsideperspectiveisattimesmostaccuratelyprovidedbythemarket.

Anothercounterargumentisthatpricesareverynoisy,orthatitisdifficulttointerpretthembecauseitisnotknownwhatkindofinformationtheyareconveying.Whilethereiscertainlynoiseinprices,theideaofthefeedbackeffectisthat,aftertakingthenoiseintoac-count,pricesarestillinformative.Rationaleconomicagentswillupdate,fullyawareofthepossibilityofnoise,andstillfindthepriceinformative.Infact,aswillbediscussedinSection2,thenoiseinpricesaddsinterestingdimensionstotheliteratureandhelpsiniden-tifyingthechannelofactivelearningfromtheprice.Similarly,asdiscussedinSection3,thereareinterestingequilibriumimplicationscomingfromthefactthattherearemultipledimensionsofinformationpotentiallyreflectedintheprice,andthesealsoenrichthestudyofthefeedbackeffect,andprovidemoredirectionsforfutureresearch.

1.3ImplicationsoftheFeedbackEffect

Traditionaltheoriesonpriceformationinfinancialmarkets(

GrossmanandStiglitz,1980

;

Hellwig,1980

;

GlostenandMilgrom,1985

;

Kyle,1985

)treatthecashflowsoftheunder-lyingassetasexogenousandunaffectedbythefinancialmarket.Thesemodelsprovidepowerfulframeworkstounderstandhowinformationisproduced,processed,and

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aggregatedinthefinancialmarketforagivenrealizationofthefirm’scashflowsandfuturevalue.Thelimitation,however,isthatthefinancialmarketinthesemodelsisessentiallyasideshow:Itreflectswhatisgoingonintherealeconomybuthasnoeffectonit.Thelitera-tureonthefeedbackeffectbuildsontheseframeworksbutchangestheparadigmtoac-countfortherealeffect:Oncedecisionmakersintherealsideoftheeconomylearnfromthemarketpriceandchangetheirdecisionsbasedonit,thepriceplaysanactiveroleinaffectingcashflowsandvaluations.

Inanearlyreviewofthefeedback-effectliterature,

Bond,Edmans,andGoldstein(2012)

discusshowitprofoundlyimpactsthetheoryoffinancialmarkets.Theymaketwomainpoints.First,thinkingaboutthefeedbackeffectchallengesthetraditionalnotionofeffi-ciencyusedtoanalyzefinancialmarkets.

Bond,Edmans,andGoldstein(2012)

distinguishbetweentwotypesofefficiency.Forecastingpriceefficiency(FPE)istheabilityofthemar-kettopredictfuturecashflows.Onewaytothinkaboutitisasthecorrelationbetweenpri-cesandfuturecashflows.Thisisinmanywaystheusualwayfinancialeconomiststhinkaboutmarketefficiency.Ontheotherhand,revelatorypriceefficiency(RPE)istheabilityofthemarkettoprovideinformationtodecisionmakersintherealsideoftheeconomythatimprovestheeconomicefficiencyoftheirinvestmentandproductiondecisions.Ithasbeenreferredtointheliteratureasrealefficiencyinshort.Whilemarketefficiencyiswhatistypicallystudied,realefficiencyisarguablywhatismorerelevantandimportanttounderstand.Interestingly,astheliteratureshows,thesetwomeasuresofefficiencyaresometimesinconflictwitheachother,implyingthatitisnotalwayssufficienttofocusonmarketefficiencyandassumethatitiscorrelatedwiththerealeconomicefficiency.

Second,incorporatingthefeedbackeffectintomodelsoffinancialmarketscanfunda-mentallychangepredictionsonhowpricesareformedandhelpunderstandingsomephe-nomenathatotherwiseseempuzzling.Oncecashflowschangeasaresultoflearningfromprices,thepriceformationprocessitselfchanges.Phenomenasuchasmanipulation,limitstoarbitrage,tradingfrenzies,andothersthenemergeinequilibrium,eveniftheydonotariseinmodelswithoutafeedbackeffect.Hence,modelingthefeedbackeffectisnotjustadetailrequiredtocompletethemodel.Rather,itchangesthefundamentalinsightsfromthemodel.

1.4LayoutoftheRestoftheArticle

Theremainderofthearticleisorganizedasfollows:InSection2,Iprovideashortreviewoftheempiricalevidenceforthefeedbackeffect.Ialsohighlightsomeofthechallengeswiththeempiricalanalysisandhowthedifferentresearchstreamsaretryingtoovercomethem.Theempiricalevidencefocusesprimarilyonshowingthatthefeedbackeffectispre-sentandnotontestingsomeofthesubtletheoreticalimplications.Hence,discussionsofinsightsfromtheoryfollowtheempiricalevidenceandarefeaturedinSection3.Inthissec-tion,Idiscussthetensionthatmodelsofthefeedbackeffectexposebetweenthedifferentnotionsofefficiencyandthendescribehowthesemodelshelpusunderstandtheoriginsofobservedphenomena.Ineachcase,theillustrationisfocusedonakeyexample,basedonarecentpaper,ormotivatedbyarecentreal-worldepisode,yettryingtohighlightthegeneralthemes.InSection4,Iprovidesomefutureperspectiveslinkedtorecentdevelopments.Specifically,Iconsiderhowtherecentrevolutionofinformationtechnologiesischangingtheinformationenvironmentinfinancialmarketsanddiscusspossibleimplicationsthrough

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thelensofthefeedback-effectliterature.Section5concludeswithadditional,moregeneral,perspectivesforfutureresearch.

2.EmpiricalEvidenceabouttheFeedbackEffect

2.1IdentifyingFeedback:TheEmpiricalChallenges

Theempiricalliteraturehasfocusedmostlyontheeffectofmarketpricesonmanagerialin-vestmentdecisions,askingwhethermanagerslearnfromthemarket.Inmanycases,such

marketfeedbackisexpectedtograduallyaffectlong-runinvestmentplansandsoisnoteas-ilyvisibleandrequirescarefulanalysis.However,onesettinginwhichmarketfeedbackiseasiertodetectisanacquisitiondecision.Here,thereistypicallyanannouncement,thenamarketreaction,andthenthefirmmaychangeitsplans.Thisiswhythesettingofanacqui-sitionisalsoonewhereanecdotesareeasiertofind.Beforedivingintotheempiricallitera-ture,letusthusconsideronerecentanecdotetofixideasofwhatmarketfeedbackmaylooklike.

OnFebruary4,2020,TheWallStreetJournalreportedthatIntercontinentalExchange(ICE),theowneroftheNewYorkStockExchange(NYSE)andotherexchangesaroundtheworldmadeatakeoveroffertoacquireeBay,apioneerine-commercewhohadbeenstrugglingtokeepupwithcompetitors.4Thiswasconfirmedlaterintheday.Inpubliccommunication,therationaleforthedealwasdescribedasbuildingonthesimilaritiesinbusinessesacrossthetwofirms,suchasmatchingbuyersandsellersorcollectingandorganizingdata.However,fortheinvestorsofICE,thiscameacrossasabadidea,giventhatthisacquisitionwoulddivertICE’sattentionfromitscorebusinessofrunningfinancialmarketsandsellingfinancialdata.Investorsvotedbysellingtheirshares,sendingICEstockpricedown7.5%onFebruary4andanother3%downonFebruary6.Withthisharshmar-ketfeedbackandadditionalconversationswithinvestors,ICEdecidedlaterthatdaytoceasetheexplorationofthisstrategicopportunity.Themarketrallied3%inresponsetothisannouncement.5

Thisexampledemonstrateshowthefeedbackfromthemarketcancausemanagerstochangethefirm’sinvestmentplans.Eventhoughmanagersareclosetothedealandconsid-eredvariousaspectsofit,suchadealisultimatelybasedonassumptionsaboutsynergiesandwhatareasonablepricemightbe.Theseareissuesonwhichinvestorsinthemarketwillhaveopinionsandtheaggregationoftheirinsightscanleadtoasignificantupdatingbythemanagers.Thiskindofdynamicsisclearlyondisplaywithsomeacquisitiondecisions.

Asthenatureofanecdotesgoes,however,onecouldsuggestalternativemechanismstobeinplay.Forexample,withtheaboveepisode,itcouldbethattherewasnoactuallearn-ing,butrathermanagersjustsurrenderedtomarketpressure.Thatis,maybetheyknewallalongthatthiswasnotagoodacquisition,butwantedtopursueitforprivatebenefits.Then,onlythestrongreactionfromthemarketmadeittoocostlytocontinueandsoledthemtoabandontheacquisition.Hence,systematicstudiesareneededtoseeabroaderpat-ternandlinkitmoreclearlytotheinformationalchannel.

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Luo(2005)

providesasystematicstudytodocumentthemanageriallearningfromthemarketonacquisitiondecisions.Heshowsthatacquisitionsaremorelikelytobecanceledwhenpricesreactmorenegativelytotheirannouncements,andthatthisisparticularlysowhentherearereasonstobelievethatmanagerscanbenefitfromlearning.Thisisthecase,forexample,whentheuncertaintyisnotabouttechnology,sincemanagershaveaclearin-formationadvantageabouttechnologybutnotnecessarilyonotheraspectsofthedeal.Giventheirdistinctnature,thedynamicsaroundmergersandacquisitionscontinuedtoplayaroleinthedevelopmentofthefeedback-effectliterature.Potentialtargetsusuallyseearun-upinpricepriortoanannouncement,andthequestioniswhatthepartiesinferfromthisandhowitaffectsmergernegotiations.

Bettonetal.(2014)

provideananalysisonthis,emphasizinglearningaboutsynergiesandexaminingtheimplicationsfortheeventualofferprice.

Thebroaderempiricalliteraturehasbeenlookingbeyondacquisitions,tryingtoseewhetherthereisanactiveroleofmarketpricesindeterminingothercorporatedecisions,suchasinvestmentsmoregenerally.Thereisasignificantempiricalchallengeindoingso.Marketpricescanbecorrelatedwithfirms’investmentsforvariousreasons,mostpromin-entlybecausebotharepositivelyaffectedbyfirms’fundamentals,withoutacausaleffectofpricesoninvestments.Theliteraturehasusedtwomainstrategiestopindownthemechan-ism.Onereliesonanalyzingtheinvestment–pricesensitivityandwhetheritiscorrelatedwithvariablesthatindicateanactiveinformationalrole.Anotheronereliesonshockstopricesthataffectthemfornon-fundamentalreasons.Below,Iwillelaborateoneachoneofthesebranchesoftheliterature.Then,Iwillbrieflydescribenewworkprovidingsurveyevi-dencefortheeffectofmarketpricesoncorporatedecisions.

2.2PriceInformativenessandInvestmentSensitivitytoStockPrice

Thefirstpaperinthisstreamofwork(

Chen,Goldstein,andJiang,2007

)analyzesthesensi-tivityofcorporateinvestmenttostockprice.Totestwhethermanagerslearnfromtheprice,itaskshowthissensitivityisrelatedtomeasuresofpriceinformativeness.Theideaisthatifinvestmentsaremoresensitivetopriceswhenpricesaremoreinformative,thenthisindi-catesthattheinformationinthepriceisusedfortheinvestmentdecisions.Twomeasuresofpriceinformativeness,whichwerecommonatthetime,wereusedfortheanalysis:pricenon-synchronicity,goingbackto

Roll(1988)

,andtheprobabilityofinformedtrading(PIN),goingbackto

Easley,Hvidkjaer,andO’Hara(2002)

.Thepaperfindsapositiverela-tionbetweenpriceinformativenessandthesensitivityofcorporateinvestmenttostockprice,consistentwiththeactivelearningstory.Inafollow-uppaper,sharpeningthemeth-odologybehindtheinvestment–pricesensitivitytests,

BakkeandWhited(2010)

provideevidencethatbolstertheseresults.

Still,anotherinterpretationoftheaboveresultsisthattheinformationinthepricewasalreadyknowntomanagerswithoutlookingattheprice,thatis,apassivecorrelationchan-nel,whichisalsoconsistentwithapositiverelationbetweenthesensitivityofinvestmenttostockpriceandpriceinformativeness.

Chen,Goldstein,andJiang(2007)

addressthisalter-nativebycontrollingformeasuresofmanagerialinformationthatarebasedoninsidertrad-ingandonthelevelofearningsurprise.Sincetheresultsarenotaffected,theevidencesupportstheactivelearningchannelmorestrongly.Laterpapersprovidefurtherteststoseparatetheactivelearningstoryfromthepassivecorrelationstory,asdescribedbelow.

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Onechallengeintheabovestrategyistofindmeasuresth